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Application of artificial intelligence in coronary CT angiography: a potential gatekeeper strategy?
Session:
Comunicações Orais (Sessão 17) - Ciência Básica e Saúde Digital
Speaker:
José Miguel Ramos Viegas
Congress:
CPC 2022
Topic:
O. Basic Science
Theme:
36. Basic Science
Subtheme:
33.1 Image Processing and Imaging Standards
Session Type:
Comunicações Orais
FP Number:
---
Authors:
José Miguel Viegas; João Ferreira Reis; Rita Teixeira; Sofia Jacinto; Tiago Mendonça; Ruben Ramos; Hugo Marques; Luísa Figueiredo; Rui Cruz Ferreira
Abstract
<p>Introduction: Coronary computed tomography angiography (CCTA) analysis plays a critical role in the diagnosis of coronary artery disease (CAD), however it can be time consuming and subject to significant inter-reader variability. Medical artificial intelligence (AI) is rapidly developing and moving from the research field to daily clinical practice. AI algorithms have demonstrated high performance and computational efficiency, reducing the degree of manual input and processing time. For its widespread clinical implementation, validation of these algorithms are highly necessary.</p> <p>Objectives: This study aimed to determine the impact of an AI-enabled CCTA analysis for comprehensive coronary artery evaluation in patients (P) with suspected CAD.</p> <p>Methods: We analysed 100 CCTA exams from a cohort of symptomatic patients (P) with mild-to-moderately abnormal non-invasive ischemia test. Stenosis severity was assessed by level III experts (manual evaluation, MEv). A novel AI-based software tool (automatic evaluation, AEv) was also used to quantify coronary stenosis and characterize plaque phenotype. In P later referred for invasive coronary angiography (ICA), diagnostic and revascularization yields of MEv and AEv were compared.</p> <p>Results: 100P, 52% male, mean age 68±10 years. One-third had persistent typical angina, of which 67% had a Canadian Cardiovascular Society angina grade ≥2. Overall prevalence of obstructive CAD determined by MEv and AEv was 25% and 21%, respectively, with a significant association between both assessments (p<0.001). Framingham risk score was significantly higher in P with obstructive stenosis (12(22) vs 24(15)%, p=0.042).</p> <p>Based upon MEv, referring physician decided to proceed to ICA in 22P (21P with significant and 1P with minimal non-obstructive stenosis). For those undergoing ICA, 13P also had obstructive CAD established by AEv. Diagnostic yields for MEv and AEv-guided ICA was 82 and 60%, and revascularization yields 73 and 60%, respectively.</p> <p>AEv atherosclerosis quantification revealed significant differences between P who did not undergo ICA, P referred for ICA without significant stenosis and P with obstructive CAD on ICA: median total plaque volume (126 vs 312 vs 518mm3, p<0.001), calcified plaque volume (23 vs 197 vs 222mm3, p<0.001), non-calcified plaque volume (71 vs 112 vs 252mm3, p<0.001) and low-density plaque volume (1.1 vs 3.0 vs 4.4mm3, p=0.042) (Fig.1).</p> <p>Conclusion: A diagnostic strategy using AI-based analysis of coronary stenosis severity on CCTA had a similar performance compared to MEv. In addition, risk prediction can be enhanced by AI assessment of plaque composition. This study is an example of the potential role of AI in the CCTA workflow. Further validation of these algorithms are needed.</p>
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